Multiple-input Multiple-output Decision Feedback Equalization Research Articles

MIMO Time-Domain Equalization for High-Speed Continuous Transmission Under Channel Variability

High-speed communication over mutually coupled channels can be severely affected by intersymbol interference (ISI) and crosstalk (XT). Multiple-input multiple-output (MIMO) equalization is known to mitigate both ISI and XT, but requires accurate channel information at the receiver (RX) and/or transmitter (TX), when the channel is stochastic. However, since adjusting the equalization filters to the specific channel realization involves a considerable implementational and computational complexity, it is desirable to keep the equalization filters fixed irrespective of the specific channel realization, especially when the channel variability is relatively small. Therefore, we propose a general MIMO transceiver scheme where the equalization filters depend on the channel statistics rather than the actual channel. More specifically, by considering the time-domain equalization with the fixed linear MIMO pre-equalization and adjustable MIMO decision feedback equalization, the complexity associated with passing channel information from the RX to the TX is avoided, and the computationally intensive and iterative calculations of the fixed pre-equalizer can be performed offline rather than on-chip. The proposed equalization scheme is shown to yield only a small performance degradation compared to fully adjustable equalization schemes, thereby enabling multi-Gbit/s chip-to-chip communication over low-cost electrical interconnects which are prone to manufacturing tolerances.

Adaptive Conjugate Gradient DFEs for Wideband MIMO Systems

New adaptive equalization algorithms for wireless systems operating over wideband multiple-input multiple-output (MIMO) channels are proposed. The problem of MIMO decision feedback equalizer (DFE) design is formulated as a set of linear equations with multiple right-hand sides (RHSs) evolving in time. By applying an adaptive modified conjugate gradient algorithm, we derive an equalizer with identical convergence, improved tracking capabilities, but higher computational load as compared to the Recursive Least Squares (RLS) algorithm. To reduce its complexity, two updating strategies of the equalizer filters based on Galerkin projections are employed.

Efficient and robust decision delays selection scheme for multiuser MIMO DFE in power imbalance situation

Recently, it has been shown that the selection of decision delays has a crucial effect on the performance of finite-length multiple-input multiple-output decision-feedback equalization (MIMO-DFE) receiver for multiuser time division multiple access (TDMA) system. With the minimum joint-BER (MJB) criterion, choosing the optimum different decision delays for individual uplink users would require multi- dimensional searching, which is too costly for real-time applications. In this paper, we propose an efficient power-ratio-based (PRB) scheme to solve the multiuser decision delay problem in practice. First, it employs the MIMO channel responses to calculate the received power ratios among uplink users. The ratios are then compared to a set of preset thresholds to determine the decision delays. To optimize the thresholds, Gaussian approximation (GA) technique is used for speeding up the off-line searching process. Under power imbalance situation, we note that the decision delays must be carefully selected on a burst-by-burst basis, or else the performance loss for the weaker users would be very significant. Hence, both the MJB and PRB methods can resist the near-far effect. Finally, simulation results show that the proposed PRB scheme has an approximate BER performance, but with much reduced computational complexity, as compared to the optimal MJB one.

Fast techniques for computing finite-length MIMO MMSE decision feedback equalizers

Multiple-input multiple-output (MIMO) digital communication systems have received great attention due to their potential of increasing the overall system throughput. In such systems, MIMO decision feedback equalization (DFE) schemes are often used to mitigate intersymbol interference (ISI) resulting from channel multipath propagation. In this context, the existing computationally efficient methods for exact estimation of the DFE filters under minimum mean-square-error criteria (MMSE-DFE) rely on fast Cholesky decomposition and backsubstitution or Levinson techniques. These methods may still present several difficulties in implementation as the demand on higher transmission rates increases, and thus a simple solution is necessary. In this paper, new procedures for fast computation of the MIMO-MMSE-DFE are presented. The new algorithms are obtained from a simple observation, namely, that the optimal feedforward filter (FFF) is related to the well-known Kalman gain matrix, commonly encountered in fast recursive least squares adaptive algorithms-for which fast recursions exist and are readily applicable. Moreover, the feedback filter can be easily computed via stable fast MIMO convolution techniques. As a result, the proposed method is less complex, more structured, and can be as reliable in finite precision as known approaches in the literature.

Near-Optimum Soft Decision Equalization for Frequency Selective MIMO Channels

In this paper, we develop soft decision equalization (SDE) techniques for frequency selective multiple-input multiple-output (MIMO) channels in the quest for low-complexity equalizers with error performance competitive to that of maximum likelihood (ML) sequence detection. We demonstrate that decision feedback equalization (DFE) based on soft-decisions, expressed via the posterior probabilities associated with feedback symbols, is able to outperform hard-decision DFE, with a low computational cost that is polynomial in the number of symbols to be recovered and linear in the signal constellation size. Building on the probabilistic data association (PDA) multiuser detector, we present two new MIMO equalization solutions to handle the distinctive channel memory. The first SDE algorithm adopts a zero-padded transmission structure to convert the challenging sequence detection problem into a block-by-block least-square formulation. It introduces key enhancement to the original PDA to enable applications in rank-deficient channels and for higher level modulations. The second SDE algorithm takes advantage of the Toeplitz channel matrix structure embodied in an equalization problem. It processes the data samples through a series of overlapping sliding windows to reduce complexity and, at the same time, performs implicit noise tracking to maintain near-optimum performance. With their low complexity, simple implementations, and impressive near-optimum performance offered by iterative soft-decision processing, the proposed SDE methods are attractive candidates to deliver efficient reception solutions to practical high-capacity MIMO systems. Simulation comparisons of our SDE methods with minimum-mean-square error (MMSE)-based MIMO DFE and sphere decoded quasi-ML detection are presented.

A Multiple Input-Multiple Output (MIMO) Adaptive Decision Feedback Equalizer (DFE) with Cancellation for Wideband Space-Time Communications

A new receiver structure able to deliver high data rates in a multiple input-multiple output (MIMO) frequency selective wireless environment is proposed and investigated in this paper. The optimal solution for the finite-length MIMO decision feedback equalizer (DFE) with cancellation is derived and used to illustrate the potential of this architecture for space-time communications. LMS and RLS adaptive algorithms are also presented for the MIMO architecture. The convergence and performance of these adaptive algorithms is analyzed through simulation results. The proposed adaptive solutions do not require channel identification, are less computationally intensive than their optimal solution counterpart, and allow the proposed MIMO receiver to seamlessly adapt to channel changes.